docstr: Pipeline to combine multiple data transformers, chaining them together.
  
  Parameters
  ----------
  transformers
      Sequence of data transformers.
  copy
      If set makes a (deep) copy of each data transformer before adding them to the pipeline
  n_jobs
      The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is
      passed as input to a method, parallelising operations regarding different ``TimeSeries``. Defaults to `1`
      (sequential). Setting the parameter to `-1` means using all the available processors.
      Note: for a small amount of data, the parallelisation overhead could end up increasing the total
      required amount of time.
      Note: this parameter will overwrite the value set in each single transformer. Leave this parameter set to
      `None` for keeping the original transformers' configurations.
  verbose
      Whether to print progress of the operations.
      Note: this parameter will overwrite the value set in each single transformer. Leave this parameter set
      to `None` for keeping the transformers configurations.
  
  Examples
  --------
  >>> import numpy as np
  >>> from darts import TimeSeries
  >>> from darts.datasets import AirPassengersDataset
  >>> from darts.dataprocessing.transformers import Scaler, MissingValuesFiller
  >>> from darts.dataprocessing.pipeline import Pipeline
  >>> values = np.arange(start=0, stop=12.5, step=2.5)
  >>> values[1:3] = np.nan
  >>> series = series.from_values(values)
  >>> pipeline = Pipeline([MissingValuesFiller(), Scaler()])
  >>> series_transformed = pipeline.fit_transform(series)
  <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
  array([[[0.  ]],
      [[0.25]],
      [[0.5 ]],
      [[0.75]],
      [[1.  ]]])
  Coordinates:
  * time       (time) int64 0 1 2 3 4
  * component  (component) object '0'
  Dimensions without coordinates: sample
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code: def __init__(
      self,
      transformers: Sequence[BaseDataTransformer],
      copy: bool = False,
      verbose: bool = None,
      n_jobs: int = None,
  ):
      """
      Pipeline to combine multiple data transformers, chaining them together.
  
      Parameters
      ----------
      transformers
          Sequence of data transformers.
      copy
          If set makes a (deep) copy of each data transformer before adding them to the pipeline
      n_jobs
          The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is
          passed as input to a method, parallelising operations regarding different ``TimeSeries``. Defaults to `1`
          (sequential). Setting the parameter to `-1` means using all the available processors.
          Note: for a small amount of data, the parallelisation overhead could end up increasing the total
          required amount of time.
          Note: this parameter will overwrite the value set in each single transformer. Leave this parameter set to
          `None` for keeping the original transformers' configurations.
      verbose
          Whether to print progress of the operations.
          Note: this parameter will overwrite the value set in each single transformer. Leave this parameter set
          to `None` for keeping the transformers configurations.
  
      Examples
      --------
      >>> import numpy as np
      >>> from darts import TimeSeries
      >>> from darts.datasets import AirPassengersDataset
      >>> from darts.dataprocessing.transformers import Scaler, MissingValuesFiller
      >>> from darts.dataprocessing.pipeline import Pipeline
      >>> values = np.arange(start=0, stop=12.5, step=2.5)
      >>> values[1:3] = np.nan
      >>> series = series.from_values(values)
      >>> pipeline = Pipeline([MissingValuesFiller(), Scaler()])
      >>> series_transformed = pipeline.fit_transform(series)
      <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>
      array([[[0.  ]],
          [[0.25]],
          [[0.5 ]],
          [[0.75]],
          [[1.  ]]])
      Coordinates:
      * time       (time) int64 0 1 2 3 4
      * component  (component) object '0'
      Dimensions without coordinates: sample
      """
  
      raise_if_not(
          all(
              (isinstance(t, BaseDataTransformer)) for t in transformers
          ),
          "transformers should be objects deriving from BaseDataTransformer",
          logger,
      )
  
      if transformers is None or len(transformers) == 0:
          logger.warning("Empty pipeline created")
          self._transformers: Sequence[
              BaseDataTransformer[TimeSeries]
          ] = []
      elif copy:
          self._transformers = deepcopy(transformers)
      else:
          self._transformers = transformers
  
      self._invertible = all(
          isinstance(t, InvertibleDataTransformer)
          for t in self._transformers
      )
  
      if verbose is not None:
          for transformer in self._transformers:
              transformer.set_verbose(verbose)
  
      if n_jobs is not None:
          for transformer in self._transformers:
              transformer.set_n_jobs(n_jobs)
  
file_path: darts/darts/dataprocessing/pipeline.py
imports: from copy import deepcopy
  from typing import Iterator, Sequence, Union
  from darts import TimeSeries
  from darts.dataprocessing.transformers import BaseDataTransformer, FittableDataTransformer, InvertibleDataTransformer
  from darts.logging import get_logger, raise_if_not
label_desc: Use image node_id 1 to create a new Pipeline object  with example: obj = Pipeline(transformers, copy, verbose, n_jobs)